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DOMESTIC AND OUTBOUND TOURISM DEMAND IN AUSTRALIA:
A System-of-Equations Approach
George Athanasopoulos1, Minfeng Deng1, Gang Li2 and Haiyan Song3
1Department of Econometrics and Business Statistics, Monash University, Clayton, VIC 3800,
Australia Emails: [email protected] (G. Athanasopoulos)
[email protected] (M. Deng)
2School of Hospitality and Tourism Management, University of Surrey, Guildford, GU2 7XH, UK Email: [email protected]
Telephone: +44 1483 686356 Fax: +44 1483 689511
3School of Hotel and Tourism Management, The Hong Kong Polytechnic University, Hong Kong Email: [email protected]
The paper should be cited as follows:
Athanasopoulos G, Deng M, Li G, Song H. (2014) 'Modelling substitution between domestic and
outbound tourism in Australia: A system-of-equations approach'. Tourism Management, 45, pp. 159-
170. doi: 10.1016/j.tourman.2014.03.018
Research Highlights • Substitution relationships between Australian domestic and outbound tourism are found • A dynamic almost ideal demand system model is employed • Long-run and short-run demand elasticities are calculated • A new price variable based on purchasing power parity index is developed • Further promotion of Australian domestic tourism is recommended
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DOMESTIC AND OUTBOUND TOURISM DEMAND IN AUSTRALIA:
A System-of-Equations Approach
Abstract
This study uses a system-of-equations approach to model the substitution relationship
between Australian domestic and outbound tourism demand. A new price variable based
on relative ratios of purchasing power parity index is developed for the substitution
analysis. Both long-run and short-run demand elasticities are calculated based on the
estimated almost ideal demand systems. The empirical results reveal significant
substitution relationships between Australian domestic tourism and outbound travel to
Asia, the UK and the US. This study provides scientific support for necessary policy
considerations to promote domestic tourism further.
Key words: domestic tourism, substitution, almost ideal demand system, purchasing
power parity, Australia
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1. INTRODUCTION
In a recent report by Tourism Research Australia in June, 2011, a widening tourism trade
deficit (calculated as the difference between total inbound revenue and total outbound
expenditure) has been identified. A peak surplus of $AU 3.6 billion in 1999-2000 has turned
into a deficit of $AU 5 billion in 2009-2010 (Tourism Research Australia, 2011a). The high
value of the Australian dollar was said to be one of the key factors in contributing to the
deficit.
For the financial year ending June 2011 international inbound tourism consumption was
estimated to be $AU23.6 billion while domestic consumption was estimated at $AU71.9
billion, three times the size of the international inbound market. The consumption of
Australian residents overseas was estimated to be $AU37.9 (Australian Bureau of Statistics,
2011). Figure 1 shows an average growth of outbound tourism consumption as a percentage
of household final consumption expenditure of approximately 4% per annum over the
period 2003-2011, in contrast to an average decline of approximately 3% per year for
domestic tourism.
“The greatest impact of the high Australian dollar on the Australian industry is the
increasing number of Australians able to afford, and choosing, to travel overseas …
this represents a significant challenge for the industry” (Tourism Australia, May
2011).
{Insert Figure 1 about here}
An initial warning of the decline in demand for the domestic market was first issued by
Athanasopoulos and Hyndman (2008). Since then a few other academic studies have
followed on the domestic market (see for example Allen, Yap, & Shareef, 2009, Divisekera,
2010, Deng & Athanasopoulos, 2011, Yap & Allen, 2011). However, despite the common
belief among Australian tourism stakeholders, as expressed by the above quote, no study
(academic or otherwise) to date has attempted to explicitly model and quantify the possible
substitution between domestic and outbound tourism demand. This study therefore aims to
fill the gap in the literature by investigating the potential substitution relationship between
Australians’ demand for domestic tourism and for international outbound tourism, based on
a theoretically sound system-of-equations model: the almost ideal demand system (AIDS)
model.
2. LITERATURE REVIEW
In the tourism economics literature, demand for international tourism has attracted
predominant research interests, while little attention has been paid to the demand for
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domestic tourism. For example, in a review article Li, Song, and Witt (2005) identified 80
empirical studies on econometric modelling and forecasting of international tourism
demand published between 1980 and 2004. Song and Li (2008) further reviewed 121
econometric and time-series analyses of international tourism demand during the period
2000-2007. However, very few publications are found to exclusively focus on domestic
tourism demand. The exceptions include Kim and Ngo (2001), Pyo, Uysal, and McLellan
(1991), and Witt, Newbould, and Watkins (1992). More recently, some research attention
has been paid particularly to the Australian domestic tourism market. For example, Huybers
(2003) applied the discrete choice modelling method to investigate the determining factors
underlying the short-break holiday destination choices of Melbourne residents.
Athanasopoulos and Hyndman (2008) used innovations state space models to forecast the
domestic tourism demand. Allen, Yap and Shareef (2009) investigated the short-run and
long-run causal relationships between economic factors and interstate tourism demand.
Divisekera (2010) applied the AIDS model to analyse the demand for different tourism
products distinguished by the motives of leisure and non-leisure tourism. Yap and Allen
(2011) explored alternative leading indicators influencing domestic tourism demand. Despite
some increasing interests in domestic tourism analysis, overall the tourism demand
literature is still dominated by international tourism demand analysis. The reasons are
related to international tourism’s greater visibility and economic significance, as well as
better data availability and quality (Pearce, 1987, Stabler, Papatheodorou, & Sinclair, 2010).
2.1 Substitution between International Tourism and Domestic Tourism
International tourism is regarded as a special sort of international trade, and its economic
significance is often discussed in relation to national balance of payments accounts. Inbound
tourism is regarded as an export, an injection to the national economic output, recorded as
a credit in the current account. On the contrary, outbound tourism is viewed as an import,
which is a leakage of a national economy and appears as a debit entry into the current
account (Seetaram, 2012, Smeral & Witt, 1996, Tribe, 2011). Therefore, a higher level of
tourism receipts from inbound tourists, along with a lower level of tourism expenditure by
outbound tourists, would contribute to the improvement of a country’s balance of payments
(Baretje, 1982, Sugiyarto, Blake, & Sinclair, 2003). On the other hand, faster growing
outbound tourism than inbound tourism would lead to greater balance of payments deficit,
as we have seen from the Australian case.
With respect to the demand for domestic tourism, it forms the “proving grounds” for the
tourism industry and contributes significantly to a destination’s tourism competitiveness. As
Crouch & Ritchie (1999, p. 141) noted, “a high domestic demand confers static efficiencies
and encourages improvement and innovation…. Foreign demand thrives more readily when
domestic tourism is well established.” In many tourist destinations, domestic tourism
contributes much more to the revenue of the tourism industry than inbound tourism does.
For example, Australian domestic tourist expenditure has generally been four to five times
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higher than the inbound tourist spending (Huybers, 2003). Although the ratio dropped to
three times in 2011, the dominance of domestic tourism is still clearly evident. In the context
of Turkey, Seckelmann (2002) argued that promoting domestic tourism is more suitable for
enhancing social sustainability and strengthening a more balanced regional development,
because “domestic tourism does not carry all of the problems linked to international mass
tourism such as external investment, outflow of the income, seasonal migration,
overcommercialization of culture etc.” (2002, p. 91).
The relationships between domestic tourism and international outbound tourism have also
been discussed in the tourism literature. For example, Pearce (1989) stressed that
substitution was a major policy issue that arose from this discussion. Given the nature of
outbound tourism in balance of payments accounts, a destination government attempts to
encourage more domestic tourism in substitution of outbound tourism. As noted by
Ashworth and Johnson (1990, p. 13), “import substitution policies may be devised to retain
more of such expenditure within the country's boundaries and thereby to raise indigenous
employment.” Similarly, Ashworth and Bergsma (1987) pointed out that governments
tended to develop tourism policies to boost domestic tourism and reduce the outflow of
foreign exchange.
The substitution between domestic and international outbound tourism can be further
explained by the microeconomic foundation of tourism demand. A prospective tourist
processes a utility function and follows a multiple-stage decision making process to reach a
final decision which maximizes his/her utility, given the budget constraint he/she faces. At
the first stage, the individual decides on travelling or not, facing the constraints of both time
and budget. He/she needs to make a trade-off between unpaid leisure and paid work,
because of the opportunity cost of leisure (Tribe, 2011). Meanwhile, he/she is constrained
by available income to be allocated between leisure and tourism on one hand and other
products on the other hand. The decision depends on the marginal rate of substitution
between tourism and non-tourism consumption with the aim to achieve the highest utility
overall (Eugenion-Martin & Campos-Soria, 2011). At the second stage, with the allocated
budget on tourism, the individual decides on the optimal allocation of the tourism budget
between domestic and international tourism. The allocated share combination is subject to
the change of relative price between domestic and international tourism. If international
tourism becomes relatively cheaper, the individual is better off in real terms and may spend
some or all of the increase in real income on international tourism (i.e., income effect).
Meanwhile, the individual may spend more on the relatively cheaper international tourism
and less on the more expensive domestic tourism (substitution effect). The optimal decision
depends on the individual’s preference and the marginal utility of prices (Stabler et al., 2010).
Thus, facing the budget constraint and an aim to maximize the utility, substitution takes
place between domestic and outbound tourism once the relative price (or real income)
changes.
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Although the substitutability between domestic and outbound tourism is well accepted,
most empirical studies on international tourism demand focused mainly on the substitution
(as well as complementarity) among alternative international destinations, and domestic
tourism was not included in the analysis explicitly (Crouch, 1996, Li et al., 2005). The
substitution between domestic tourism and outbound tourism was considered in an implicit
way by specifying a relative price variable—the destination’s price level against the domestic
price level of the origin country, adjusted by relevant exchange rates (e.g., Seetaram, 2012,
Smeral & Witt, 1996). In such a way the substitutability of domestic tourism for international
tourism is assumed to exist rather than to be verified.
A number of studies have explicitly explored the distinction between domestic and
outbound tourism, for instance, Eugenion-Martin & Campos-Soria (2011), Nicolau & Más
(2005), van Soest & Kooreman (1987). In these studies, survey data at the individual tourist
level was used, and a multiple-stage decision making process was assumed. Domestic
tourism and outbound tourism were explicitly distinguished by the different degrees of
impacts of the income and demographic variables. But the cross-sectional data used in these
studies excluded price variables. Therefore, without estimating cross-price elasticities, these
studies did not explicitly measure the extent of substitution between domestic and
outbound tourism demand.
On the other hand, using aggregate time-series data, Hamal (1997) and Holmes and
Shamsuddin (1997) quantified the substitution effect between travelling domestically and
abroad. Both studies used single-equation demand models, in which both domestic and
foreign tourism prices were included, thus allowing for estimation of cross-price elasticities
and verification of substitutability. The limitations of single-equation demand models are
well noted, such as lacking an explicit basis in consumer demand theory, and being unable to
test such theoretical restrictions as symmetry and adding-up that are associated with
existing demand theories (Li, Song, & Witt, 2004, 2006). Hamal (1997) acknowledged that
the most appropriate way to investigate the substitutability is to estimate a complete
demand system that is consistent with economic theory. However, due to data limitations,
Hamal (1997) did not implement system modelling. The AIDS model, with its rigorous
theoretical underpinning, can serve this research purpose well, and is thus selected for the
present study. This study serves as the first attempt in the context of analysing the
substitution between Australian domestic and international outbound tourism with a
system-of-equations approach.
2.2 AIDS Modelling
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The AIDS model was originally developed by Deaton and Muellbauer (1980). Economic
theory of consumers’ demand underpins the AIDS model, in which all goods under
investigation are represented in a system of equations and are analysed simultaneously.
Expenditure elasticities, own-price elasticities, and cross-price elasticities can be calculated
using AIDS estimates. In particular, the sign of the calculated cross-price elasticity indicates
the relationship between the two products being concerned. A positive sign suggests
substitution while a negative sign suggests complementarity. In addition, the homogeneity
and symmetry restrictions of demand theory can be tested within the AIDS framework.
Applications of AIDS modelling in tourism studies have largely been of a static form (from
here on referred to as STATIC-AIDS), in which case the results can be interpreted as the long-
run behaviour of tourism demand if the cointegration relationship holds (see De Mello, Park,
& Sinclair, 2002, Han, Durbarry, & Sinclair, 2006, Papatheodorou, 1999).
However, various demand studies have shown that demand systems often deviate from
their long-term equilibrium (Anderson & Blundell, 1983; Lyssiotou, 2000, Duffy, 2003, De
Mello and Fortuna, 2005). Thus the STATIC-AIDS specification, without incorporating the
dynamic adjustment mechanism in the short run, often fails the tests for theoretical
restrictions such as homogeneity and symmetry, and the subsequent long-run elasticity
estimates may not be accurate (Edgerton, Assarsson, Hummelmose, Laurila et al., 1996). In
tourism studies, Cortés-Jiménez, et al. (2009), Durbarry and Sinclair (2003), Li, et al. (2004,
2006) and Wu, Li, and Song (2011) incorporated error correction (EC) mechanisms into AIDS
modelling (from here on referred to as EC-AIDS model). Empirical evidence has shown that
EC-AIDS models can improve theoretical conformability as well as the forecasting
performance (see, for example, Cortés-Jiménez et al., 2009, Li et al., 2004).
The applications of AIDS models in international tourism studies can be broadly grouped into
two types of demand analyses: (a) tourists’ expenditure allocation among a number of
selected international destinations (e.g., De Mello et al., 2002, Han et al., 2006,
Papatheodorou, 1999); (b) tourists’ budget allocation among different product categories
such as accommodation, food and drinks, and shopping in an outbound destination (e.g.,
Fujii, Khaled, & Mak, 1985, Wu et al., 2011). The substitution or complementary
relationships among different outbound destinations and among different tourism product
categories can be investigated through the calculated cross-price elasticities. In the studies
where alternative outbound destinations were considered, the price variables of the AIDS
model often takes a relative form, that is, the price of each destination relative to that of the
country of origin (i.e., domestic tourism price). The substitution relationship between
domestic and outbound tourism is not explicitly tested as their main objective was to
analyse the level of competition amongst outbound destinations and the domestic equation
is not included. Thus far, no attempt has been made to carry out any empirical study using
this system-of-equations model, in which domestic destinations and outbound destinations
are explicitly treated as potentially substitutable products. This paper therefore aims to
bridge the gap in the literature. Given the alarming situation of domestic tourism in Australia
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as outlined in the Introduction, this study aims to investigate the substitution relationships
between Australian domestic tourism and outbound tourism in key destination countries
and regions, and draw some useful policy implications based on the empirical findings.
3. METHODOLOGY
Assuming a priori weak separability of goods and services, an assumption necessary for
practical reasons and common in demand system studies (Pollak & Wales, 1992), we argue
that tourism consumption of Australians follows a three-stage budgeting process, much in
line with the past literature such as Eugenio-Martin and Campos-Soria (2011), Nicolau and
Más (2005) and van Soest and Koorerman (1987). In Stage one, Australians budget for
tourism spending and non-tourism spending. In Stage two, Australians budget between
spending on domestic tourism and outbound tourism. In Stage three, specific destination
choice either domestically or abroad is considered. Given the research aim and data
limitations, Stages two and three are combined in the following empirical model.
Practicality and data availability dictate that outbound tourism cannot be looked at for all
the countries that Australians visit. We select the following key outbound destination
countries: the US, the UK, New Zealand, Japan, Hong Kong, Singapore, Malaysia, Indonesia,
and Thailand. Total outbound tourism expenditure to these destination countries
contributed to more than 50% of total Australian outbound expenditure over our sample
period. Ideally, we would like to have included more countries. Unfortunately samples taken
by Tourism Australia do not cover some of the less popular destination countries or these
are simply grouped as “Others”. This makes their exclusion from our AIDS model inevitable
as we do not have the necessary price information for these. Furthermore, given the
relatively short time series length, the six Asian countries are combined as “Asia” region. We
present details on this in the Section 3.7. In what follows we provide a brief explanation of
the STATIC-AIDS and EC-AIDS specifications, and then pay particular attention to the
development of a new price variable for this framework.
3.1 STATIC-AIDS
A static AIDS model for modelling the demand of tourism by Australians can be written as
𝑤𝑖𝑡 = 𝛼𝑖 + ∑ 𝛾𝑖𝑗𝑙𝑛𝑝𝑗𝑡𝑗 + 𝛽𝑖𝑙𝑛𝑥𝑡
𝑝𝑡+ 휀𝑖𝑡, (1)
Where 𝑤𝑖𝑡 is tourism destination i’s budget share in Australian tourism expenditure at time
t); 𝑝𝑗𝑡 is the price measure of tourism products at tourist destination j at time t; 𝑖, 𝑗= 1,…,N
denote tourist destinations: Australia (for domestic tourism), Asia, US, UK, and New Zealand,
respectively; N=5 the number of total tourist destinations considered; 𝑥𝑡 is the total
expenditure on all tourist destinations in the system at time t; 𝑝𝑡 is the aggregate price index
at time t; 𝑥𝑡
𝑝𝑡 is the real total expenditure per capita at time t; and 휀𝑖𝑡 is the iid normal
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disturbance term.𝛼𝑖, 𝛾𝑖𝑗, and 𝛽𝑖 are unknown parameters to be estimated. 𝛾𝑖𝑗’s represent
the long-run effect of price changes on the budget shares of various tourist destinations
while the 𝛽𝑖’s represent the long-run effect of the overall expenditure budget changes on
the budget allocation.
Among a number of aggregate price indices being proposed in the literature, the Tornqvist
(1936) index has superior properties such as being exact for linearly homogeneous functions
and is more likely to generate unbiased expenditure and price elasticities (see Wu et al.
(2011) for further discussions). Therefore in this study we use the Tornqvist index, which is
computed as
𝑝𝑡 = ∏ (𝑝𝑖𝑡
𝑝𝑖0)
�̃�𝑖𝑡𝑁𝑖=1 ,
with weight �̃�𝑖𝑡 is defined as
�̃�𝑖𝑡 = (𝑤𝑖𝑡 + 𝑤𝑖0)/2,
where 𝑤𝑖’s are positive and add up to 1, and t=0 represents the base period.
3.2 EC-AIDS
The STATIC-AIDS model of equation (1) assumes that the consumption behaviour of
Australian tourists is always in equilibrium. However, in the short term the demand system
is likely to be in disequilibrium. Therefore, to capture tourists’ dynamic behaviours over time,
a dynamic demand system should incorporate the mechanism of short-run adjustment
towards the long-run equilibrium. Past AIDS studies employed various dynamic
specifications (e.g., Anderson & Blundell, 1983, Durbarry & Sinclair, 2003, Lyssioutou, 2000).
The following EC-AIDS specification is widely used in past empirical studies (e.g., Chambers
& Nowman, 1997, Duffy, 2003) and has been shown to be appropriate for tourism demand
analysis (e.g., Durbarry & Sinclair, 2003, Li, et al., 2006).
𝐴(𝐿)𝑤𝑡 = 𝐵(𝐿)𝑧𝑡 + 휀𝑡 (2)
where 𝑤𝑡 is a (N x 1) vector of budget shares observed at time t, and 𝑧𝑡 is a [(N + 2) x 1]
vector of intercepts, N price variables, and real expenditure per capita, observed at time t.
𝐴(𝐿) = 𝐼 + ∑ 𝐴𝑘𝑙𝑘=1 𝐿𝑘 and 𝐵(𝐿) = 𝐼 + ∑ 𝐵𝑘
𝑚𝑘=0 𝐿𝑘 are matrix polynomials in the lag
operator L. In theory, information criteria can be used to determine the optimal lag lengths
of l and m, starting with arbitrarily high orders. In practice, demand systems are often
heavily parameterized with limited number of observations in the time dimension, which
makes the sequential testing of lag lengths impossible. The EC-AIDS model of Equation (2)
suggests that current budget share movements depends on not only current changes both
standard AIDS explanatory variables (i.e., 𝑝𝑗𝑡 and 𝑥𝑡
𝑝𝑡 ) but also adjustments to consumer
9
disequilibrium in the previous period through an error correction process (Durbarry &
Sinclair, 2003).
Given the short length of the time series, we restrict our model to a parsimonious first order
system in levels. In its error correction form, Equation (2) becomes a dynamic EC-AIDS model:
∆𝑤𝑖𝑡 = 𝜆𝜇𝑖,𝑡−1 + ∑ 𝛾𝑖𝑗∗ ∆𝑙𝑛𝑝𝑗𝑡𝑗 + 𝛽𝑖
∗∆𝑙𝑛𝑥𝑡
𝑝𝑡+ 휀𝑖𝑡
∗ (3)
Where ∆ is the difference operator. 𝜇𝑖,𝑡−1 is the error correction term that measures the
disequilibrium for the 𝑖th budget share equation in the previous period, and it is the
estimated residual from the 𝑖th STATIC-AIDS equation. 𝜆 measures the extent to which the
ith equation adjusts to its own budget share allocation disequilibrium at time t-1. As a
parsimonious specification and in line with past empirical studies such as Durbarry and
Sinclair (2003) and Edgerton et al. (1996), the value of 𝜆 is assumed to be identical for all
equations. This implies a restricted assumption that the speed of consumers’ short-run
adjustment to the long-run equilibrium is the same across all products in the system. A full
specification would take into account that the change of each expenditure share adjusts
with respect to not only its own disequilibrium, but also the disequilibrium of each of the
other products in the system (Duffy, 2003). However, such a full specification consumes a
large number of degrees of freedom, which has restricted its application in tourism demand
studies. It is easy to see that Equation (3) captures both short-run and long-run dynamics. In
the short run, where disequilibrium exists, budget share responds to changes in prices, real
expenditure per capita, and disequilibrium from the previous period. In the long run, where
the system reaches its steady state, all differenced terms become zero, in which case
Equation (3) becomes Equation (1).
3.3 Theoretical Restrictions and Estimation
The AIDS models are derived from economic demand theory, and as a result a set of
theoretical restrictions must be satisfied (Deaton & Muellbauer, 1980). More specifically,
they are:
(i) Adding up:
∑ 𝛼𝑖
𝑖
= 1; ∑ 𝛽𝑖
𝑖
= 0; ∑ 𝛾𝑖𝑗
𝑖
= 0 in Equation(1)
∑ 𝛽𝑖∗
𝑖
= 0; ∑ 𝛾𝑖𝑗∗
𝑖
= 0 in Equation(3)
(ii) Homogeneity:
∑ 𝛾𝑖𝑗
𝑗
= 0 in Equation (1)
10
∑ 𝛾𝑖𝑗∗
𝑗
= 0 in Equation(3)
(iii) Slutsky symmetry:
𝛾𝑖𝑗 = 𝛾𝑗𝑖in Equation (1)
𝛾𝑖𝑗∗ = 𝛾𝑗𝑖
∗ in Equation(3).
The adding-up condition implies that budget shares must always sum to unity. The
homogeneity condition implies that a proportional change in all prices and real expenditure
does not alter quantities purchased and budget allocations. The symmetry condition implies
a symmetric substitution matrix and consumer preferences are consistent. The adding-up
condition needs not be tested and is easily satisfied by omitting one equation from the
system when estimating the model. The coefficients of the omitted equation can be
calculated after using the adding-up rule if needed. Homogeneity can be tested equation-by-
equation, while symmetry can only be tested for the entire system as it involves cross-
equation restrictions. Both STATIC-AIDS and EC-AIDS models are typically estimated using
Zellner’s (1962) seemingly unrelated regression procedure, which accounts for
contemporaneous correlations across equations and is more efficient than equation by
equation ordinary least squares estimation.
3.4 Calculation of Demand Elasticities
Demand elasticities are computed using coefficient estimates from the AIDS model.
Specifically,
𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒 𝑒𝑙𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦: 휂𝑖 =𝛽𝑖
𝑤𝑖+ 1
𝑢𝑛𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑒𝑑 𝑝𝑟𝑖𝑐𝑒 𝑒𝑙𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦: 𝜓𝑖𝑗 =𝛾𝑖𝑗 − 𝑏𝑖𝑤𝑗
𝑤𝑖− 𝛿𝑖𝑗
𝑐𝑜𝑚𝑝𝑒𝑛𝑠𝑎𝑡𝑒𝑑 𝑝𝑟𝑖𝑐𝑒 𝑒𝑙𝑎𝑠𝑡𝑖𝑐𝑖𝑡𝑦: 𝜓𝑖𝑗 =𝛾𝑖𝑗 + 𝑤𝑖𝑤𝑗
𝑤𝑖− 𝛿𝑖𝑗
where 𝛿𝑖𝑗 is the Kronecker delta, which is a function of index i and j. 𝛿𝑖𝑗 is equal to 1 for
𝑖 = 𝑗, and 0 otherwise. As budget shares are observed T times during the sample period, all
elasticities are calculated at the sample means of the budget shares. Standard errors of the
elasticities are computed based on the estimated variance covariance matrix of the AIDS
model’s coefficients. It should be noted that, the uncompensated price elasticity measures
how a change in the price of one product affects the demand for this product and other
products with the total expenditure and other prices held constant, while the compensated
price elasticity measures the price effect on the demand assuming the real expenditure (x/p)
remains constant. In particular, the sign of a calculated compensated elasticity indicates the
11
substitutability or complementarity between the products under consideration (Edgerton et
al., 1996). Therefore, this study focuses on compensated price elasticities.
3.5 A Commonly Used Price Variable
In many demand studies involving prices measured at different countries denominated in
different currencies, the price variable for origin country i relative to destination country j is
constructed as:
𝑝𝑗,𝑡 =𝐶𝑃𝐼𝑗,𝑡 𝐶𝑃𝐼𝑖,𝑡⁄
𝐸𝑋𝑗,𝑡 𝐸𝑋𝑖,𝑡⁄ (4)
where at time 𝑡, 𝐶𝑃𝐼𝑗,𝑡 and 𝐶𝑃𝐼𝑖,𝑡 are consumer price indices as proxies of tourism prices for
the destination country 𝑗 and the origin country 𝑖 , respectively;. 𝐸𝑋𝑗,𝑡 and 𝐸𝑋𝑖,𝑡 are
exchange rates against the US dollar for the destination country and the origin country; all
indexed to be 100 for a given base year (e.g., Han et al., 2006). In our empirical study, the
origin country i is always Australia. A higher rate of inflation in country j would result in a
higher 𝐶𝑃𝐼𝑗,𝑡 and a larger 𝑝𝑗,𝑡 over time, reflecting the higher cost for Australians travelling
to destination j. On the other hand, depreciation of country j’s currency would result in a
higher 𝐸𝑋𝑗,𝑡 and a smaller 𝑝𝑗,𝑡, representing a reduced cost for Australians travelling to
destination j. Thus, 𝑝𝑗,𝑡 as computed in Equation (4) can adequately capture the temporal
price movements of destination country j relative to original country i.
{insert Figure 2 here}
However, this price variable is not without its limitations. Figure (2) shows the quarterly
prices constructed using Equation (4) for the five tourist destinations under consideration in
our study. For instance, the price series for UK is computed as: 𝑝𝑈𝐾,𝑡 =𝐶𝑃𝐼𝑈𝐾,𝑡 𝐶𝑃𝐼𝐴𝑈𝑆,𝑡⁄
𝐸𝑋𝑈𝐾,𝑡 𝐸𝑋𝐴𝑈𝑆,𝑡⁄. Firstly,
the domestic series exhibits no price movements. This is due to the fact that for domestic
travellers, destination j of the tourist and origin i of the tourist are the same, which cancels
out in both the numerator and the denominator. This is not ideal, as temporal price
movements are needed if one were to estimate both own and cross price elasticities for
domestic prices.
Secondly, these prices cannot be used to represent the relative levels of the tourist
destinations, and are therefore not directly comparable. For instance, Asia is shown to have
a higher price level than the US, while UK would be the cheapest tourist destination from
2009 onwards, which are both unlikely to be true. Moreover, both 𝐶𝑃𝐼𝑗,𝑡 and 𝐸𝑋𝑗,𝑡 in
Equation (4) are indexed to a certain base year. If the base year chosen for 𝐶𝑃𝐼𝑗,𝑡 is the same
as that chosen for 𝐸𝑋𝑗,𝑡 for all destinations, 𝐶𝑃𝐼𝑗,𝐵𝑎𝑠𝑒𝑌𝑒𝑎𝑟 = 𝐸𝑋𝑗,𝐵𝑎𝑠𝑒𝑌𝑒𝑎𝑟 = 100 ∀ 𝑗. We
would have a curious result where all destination countries have the same price value for
that particular base year. As highlighted in Forsyth and Dwyer (2009), “this (relative price
12
variable) involves a measure of changes in price competitiveness, but it does not measure
the actual of price competiveness”.
3.6 A PPP-based Price Variable
Forsyth and Dwyer (2009) provided a comprehensive summary of potential price measures
for tourism competitiveness studies. Of the list of indicators that were found to provide
meaningful cross-country comparisons, purchasing power parity (PPP), which is defined as
“the number of currency units required to buy goods equivalent to what can be bought with
one unit of the currency of the base country; or with one unit of the common currency of a
group of countries” (United Nations, 1992), was identified as the most broadly available and
general indicator. Therefore, in the context of AIDS modelling for tourism demand, we
propose a new price variable constructed based on PPP.
Specifically, we replace CPI in Equation (4) with PPP:
𝑃𝑗,𝑡 =𝑃𝑃𝑃𝑗,𝑡 𝑃𝑃𝑃𝑖,𝑡⁄
𝐸𝑋𝑗,𝑡 𝐸𝑋𝑖,𝑡⁄× 100 (5)
where 𝑃𝑃𝑃𝑗,𝑡 and 𝑃𝑃𝑃𝑖,𝑡 are PPP observed at time t in tourist destination j and tourist origin
i (which in our case is always Australia) respectively. But just as in the case of the price
variable based on CPI/EX ratios, 𝑃𝑗,𝑡 from Equation (5) will always be equal to 1 for the
domestic price series as j = i = Australia for all t. To circumvent this problem, we note that
the Penn World Table (PWT) version 7.0, which periodically releases measures of PPP and
national accounts (see Deaton & Heston, 2010, for a detailed discussion), also publishes the
following two measures:
𝐶𝐺𝐷𝑃𝑗,𝑡
which is called the PPP converted GDP per capita of country j at current prices in USD, and it
is essentially the ratio of GDP per capita over PPP; and
𝑅𝐺𝐷𝑃𝑗,𝑡|2005
which is called the PPP converted GDP per capita of country j at 2005 constant prices in USD
and is derived using the Laspeyres method. We suggest computing the following measure:
𝑃𝑃𝑃𝑗,𝑡|2005 =𝐶𝐺𝐷𝑃𝑗,𝑡
𝑅𝐺𝐷𝑃𝑗,𝑡|2005× 𝑃𝑃𝑃𝑗,𝑡
which we shall call PPP at 2005 constant prices. 𝑃𝑃𝑃𝑗,𝑡|2005 is a PPP measure augmented by 𝐶𝐺𝐷𝑃𝑗,𝑡
𝑅𝐺𝐷𝑃𝑗,𝑡|2005, which is a GDP ratio of GDP at current prices and GDP at prices of a base year
(2005 in this case). Finally, in Equation (5) replacing 𝑃𝑃𝑃𝑗,𝑡 with 𝑃𝑃𝑃𝑗,𝑡|2005 results to
𝑃𝑗,𝑡|2005 =𝑃𝑃𝑃𝑗,𝑡|2005 𝑃𝑃𝑃𝑖,𝑡⁄
𝐸𝑋𝑗,𝑡 𝐸𝑋𝑖,𝑡⁄× 100, (6)
13
the new price variable used in our empirical study. Compared to the commonly used price
variable of Equation (4), the new price variable allows: (a) valid cross-sectional comparison
of price levels as it is based on PPP measures; (b) temporal variations irrespective to which
country is chosen as the base country, as it is augmented by a GDP ratio denominated by a
fixed base year price level. Figure (3) shows yearly evolutions of the new relative price
variables for the five tourist destinations in our study from year 2000 to year 2009. Asia is
seen as the least expensive destination, while Australia shows a steady increasing trend over
time. In comparison to Figure (2), these price series are much more in line with our prior
expectation on the relative levels of the tourist destinations in our study.
{Insert Figure 3 about here}
3.7 Data Description
We measure Australian tourism demand with overnight expenditure. For domestic tourism,
we use the aggregate expenditure by all purposes of travel and across all states. In terms of
outbound demand, we consider the US, the UK, New Zealand, and Asia as the four main
overseas destinations. For Asia we include Japan, Hong Kong, Singapore, Malaysia, Indonesia,
and Thailand. The reason for combining six Asian countries into one destination region is
due to the short data series available. Including these countries individually in the AIDS
models would consume over 50 more degrees of freedom, and thus a simultaneous
estimation of the AIDS models would be infeasible.
Therefore we compute the price variable for ASIA as the weighted average,
𝑃𝐴𝑆𝐼𝐴,𝑡|2005 = ∑ 𝑃𝑗,𝑡|2005
6
𝑗=1
𝑤𝑗,𝑡
where 𝑗 = 1,2, … ,6 represent Japan, Hong Kong, Singapore, Malaysia, Indonesia, and
Thailand respectively. 𝑝𝑗,𝑡|2005 is the price level of the jth country at time t, as given by
Equation (6), and 𝑤𝑗,𝑡 is the tourism expenditure budget share of the jth country (see Song
et al., 2010, and references therein, for similar implementations of such weighted averages
in the tourism literature).
We have quarterly expenditure data covering the period of 2000Q1 to 2010Q3, giving us 43
observations in the time dimension. We seasonally adjust the data prior to estimation using
the multiplicative method in EViews 7. Both domestic and outbound data come from the
National Visitors Survey managed by Tourism Research Australia (Tourism Research
Australia, 2010).
Figure 4 shows the budget share for Australian domestic tourism over the sample period. It
is clear that the expenditure share of domestic tourism has experienced a steady decline
over the decade. From a peak of over 80% during 2000-2004 it has fallen to about 70% in
year 2010.
14
{Insert Figure 4 about here}
Figure 5 shows the tourism budget shares of the overseas destinations. In contrast to
domestic tourism expenditure shares of overseas destinations all experienced increases,
suggesting that Australians are increasingly substituting domestic travel with overseas travel.
In particular, Asia has the highest budget share amongst all four overseas destinations. This
is due to a combination of factors such as its close spatial proximity, relatively lower costs,
and strong cultural connections due to the presence of a large Asian migrant community in
Australia.
{Insert Figure 5 about here}
In terms of the price variables, as PWT version 7.0 only publishes PPP data at annual
frequency, the modified relative price level of GDP of Equation (6) can only be calibrated
annually. In order to apply this price variable in our study, we need to convert it to quarterly
frequency. We model the modified relative price level of GDP of Equation (6) to be a
function of: the domestic price level of country 𝑗, the Australian price level and the exchange
rate of country j denoted in Australian dollar (AUD). Therefore we write:
𝑃𝑗,𝑡|2005𝑦𝑟
= 𝛽0 + 𝛽1𝐶𝑃𝐼𝑗,𝑡𝑦𝑟
+ 𝛽2𝐶𝑃𝐼𝐴𝑈𝐷,𝑡𝑦𝑟
+ 𝛽3
𝐸𝑋𝑗,𝑡𝑦𝑟
𝐸𝑋𝐴𝑈𝑆,𝑡𝑦𝑟 + 𝑒𝑡 (7)
where 𝑃𝑗,𝑡|2005𝑦𝑟
, 𝐶𝑃𝐼𝑗,𝑡𝑦𝑟
, 𝐶𝑃𝐼𝐴𝑈𝐷,𝑡𝑦𝑟
, and𝐸𝑋𝑗,𝑡
𝑦𝑟
𝐸𝑋𝐴𝑈𝑆,𝑡𝑦𝑟 , are observations at the yearly frequency.
Equation (7) is estimated by OLS. The R2s for each of the regressions are respectively:
99.24%, 95.87%, 86.22%, 87.69% and 97.35%, showing satisfactory model fits.
We next project this relationship based on annual averages to a relationship based on
quarterly averages,
�̂�𝑗,𝑡|2005𝑞𝑟𝑡 = �̂�0 + �̂�1𝐶𝑃𝐼𝑗,𝑡
𝑞𝑟𝑡 + �̂�2𝐶𝑃𝐼𝐴𝑈𝐷,𝑡𝑞𝑟𝑡 + �̂�3
𝐸𝑋𝑗,𝑡𝑞𝑟𝑡
𝐸𝑋𝐴𝑈𝑆,𝑡𝑞𝑟𝑡 (8)
where �̂�0, �̂�1, �̂�2, and �̂�3 are the OLS estimates from Equation (7). �̂�𝑗,𝑡|2005𝑞𝑟𝑡 is the fitted
quarterly value of the price variable, and 𝐶𝑃𝐼𝑗,𝑡𝑞𝑟𝑡, 𝐶𝑃𝐼𝐴𝑈𝐷,𝑡
𝑞𝑟𝑡 and 𝐸𝑋𝑗,𝑡
𝑞𝑟𝑡
𝐸𝑋𝐴𝑈𝑆,𝑡𝑞𝑟𝑡 are all observed
quarterly values.
Finally, we apply the following standardization factor:
휁𝑗,𝑡𝑞𝑟𝑡 =
𝑃𝑗,𝑡|2005𝑦𝑟
∑ �̂�𝑗,𝑡|2005𝑞𝑟𝑡
𝑞𝑡𝑟 /4
where ∑ �̂�𝑗,𝑡|2005𝑞𝑟𝑡
𝑞𝑡𝑟 /4 results to the annual average aggregate of the quarterly fitted values
within each year so that
15
�̂̂�𝑗,𝑡|2005𝑞𝑟𝑡
= �̂�𝑗,𝑡|2005𝑞𝑟𝑡
× 휁𝑗,𝑡𝑞𝑟𝑡
is our proposed modified relative price level of GDP (as defined in Equation 6) fitted for
quarterly data. Note that 휁𝑗,𝑡𝑞𝑟𝑡 is merely an adjustment factor. It guarantees that the sum of
the fitted quarterly prices for any given year is equal to the observed yearly value.
Since 𝑃𝑗,𝑡|2005𝑦𝑟
is only observed up to year 2009, quarterly prices in year 2010 are forecasted
using Equation (8). Figure 6 displays the yearly 𝑃𝑗,𝑡|2005𝑦𝑟
, the quarterly �̂�𝑗,𝑡|2005𝑞𝑟𝑡 , and modified
quarterly values �̂̂�𝑗,𝑡|2005𝑞𝑟𝑡 for the domestic price variable as an example. The �̂�𝑞𝑟𝑡 series
projects the relationship in Equation (7) estimated at the yearly frequency to quarterly
values. �̂̂�𝑞𝑟𝑡 are the standardised values, so that the sum of quarterly values within a year is equal to the observed yearly value. It should be noted that the approach of interpolation and adjustment we apply here is in the same spirit as Chow and Lin (1971, 1976), Fernandez (1981), and Litterman (1983), in which OLS estimates based on low-frequency data (in our case annual data) are used as best linear unbiased interpolators of high-frequency data (in our case quarterly data) augmented by summation equality between annual and quarterly values. Abeysinghe and Lee (1998) also apply such methods in order to interpolate Malaysian annual GDP to quarterly.
{Insert Figure 6 about here}
Figure 7 displays the quarterly modified relative price level of GDP for all the destinations
considered in this study. Overall the price level of Asia remains low compared to the other
destinations, although the gap has closed considerably except against domestic Australian
tourism. The price level of Australia has experienced a steady increase over the years. The
price level of New Zealand followed a similar trajectory to that of Australia until roughly year
2006, when its price increase slowed and started to become relatively cheaper compared to
Australia. Both the US and the UK were much more expensive than the other three
destinations at the beginning of the sample period, but the gap was significantly reduced
over the decade. While the US, the UK, and NZ are still more expensive than Asia in general,
all four destinations are now cheaper than Australia. Finally it is also clear that the domestic
price series is a lot less volatile than the price series for overseas destinations. This is to be
expected, as from an Australian perspective, variations in exchange rates would play no part
in the price level of domestic travels.
{Insert Figure 7 about here}
4. EMPIRICAL RESULTS
Both long-run STATIC-AIDS and short-run EC-AIDS models are estimated, and theoretical
restrictions are tested. Based on the final model estimates, long-run and short-run demand
elasticities are calculated. In particular, cross-price elasticities quantify the degree of
substitution between domestic tourism and outbound tourism to key destinations.
4.1 Model Estimation and Theoretical Restriction Tests
16
To estimate the STATIC- and EC-AIDS models, we initially exclude New Zealand from our
system estimation. The coefficients of the omitted New Zealand equation are subsequently
calculated using the adding-up rule. We sequentially test for theoretical restrictions (as in
Wu, et al., 2011). Specifically, we first estimate the AIDS models fully unrestricted. We then
estimate the models with homogeneity restrictions imposed. Finally, we estimate under
both homogeneity and symmetry. The first test is carried out via imposing homogeneity on
the fully unrestricted model, the second test is carried out via imposing symmetry
restrictions on the homogeneity-restricted model, and the third test is carried out via
imposing both symmetry and homogeneity restrictions on the fully restricted model. For the
STATIC-AIDS model, the Wald statistics are summarized in the first column of Table1. None
of the theoretical restrictions are satisfied for the STATIC-AIDS specification as the null
hypothesis is rejected at a 1% level of significance in all three tests. The lack of short-run
dynamics in the model is most likely the cause of these restrictions not being satisfied as the
past literature has argued (e.g., Edgerton et al., 1996).
{Insert Table 1 about here}
We estimate the EC-AIDS with SUR using the residual series generated from the estimated
STATIC-AIDS model. In order for the error correction representation of the EC-AIDS model to
be valid, long-run equilibrium relationships of the demand system must be established, i.e.,
the residual series from STATIC-AIDS estimation have to be stationary. The Augmented
Dickey-Fuller (ADF) test is applied to test for the stationarity of the residual series. The
residual series of the Domestic equation is found to be stationary at 5% significance level,
while the residual series of all remaining equations are found to be stationary at 1%
significance level. This suggests that the STATIC-AIDS model provides an adequate
representation of the long-run equilibrium state of the system and short-run dynamics can
be represented in an error correction form. Again we test for the theoretical restrictions
sequentially, and the Wald statistics are summarized in the second column of Table 1.
The results show that the EC-AIDS specification passes all the restriction tests, at least at the
1% significance level. This suggests that the theoretically restricted EC-AIDS model is
adequately specified and the calculated elasticities reflect accurately Australian residents’
short-term tourism consumption behaviour. We present estimation results for both the
long-run model (in Table 2) and the short-run model (in Table 3). Even though the static
long-run model does not pass the tests of theoretical restrictions, and the estimated long-
run elasticities may not be precise, they still provide a useful foundation for comparisons
against the results of the dynamic short-run model, and for drawing some general policy
implications. It should be noted that we report the restricted STATIC-AIDS in Table 2, and it
is the residual terms of this model that were incorporated into the EC-AIDS. Even though the
STATIC-AIDS failed the restriction tests, it is believed that the reason is mainly related to the
model specification, and these theoretical restrictions should be considered valid in the long
run. With regard to the estimates of the EC-AIDS, the EC term in the EC-AIDS is significant at
17
the 1% level, suggesting the presence of strong short-run dynamics. Its negative value (-
0.621) is consistent with an error correction mechanism.
{Insert Table 2 about here}
{Insert Table 3 about here}
4.2 Long-Run and Short-Run Elasticities
As have been shown earlier, the EC-AIDS model satisfies both homogeneity and symmetry
restrictions, thus these results are likely to be more representative of the true consumer
behaviour of Australian tourists in the short run. Long-run elasticities may be subject to
some degrees of inaccuracy, but they still provide useful indications on the general patterns
of Australian tourists’ long-run tourism consumption behaviour. Table 4 summarizes the
long-run and short-run expenditure elasticity estimates from the dynamic EC-AIDS model. All
estimated expenditure elasticities are positive and statistically significant. In line with
economic theory and past empirical studies (e.g., Li et al., 2004), among all destinations
long-run elasticities are higher than their short-run counterparts except for domestic
tourism, in which case long-run and short-run elasticities are still very close. The estimated
expenditure elasticity for domestic tourism is less than 1, consistent with the general belief
that domestic tourism is treated as a necessity. The expenditure elasticities of the three of
the four overseas destinations (Asia, the US, and the UK) are all greater than 1, consistent
with past literature which commonly suggests that international tourism is considered a
luxury good (Li et al., 2005). Lastly, the expenditure elasticity of New Zealand is less than 1,
suggesting that ustralian tourists treat tourism in New Zealand as a necessity, which is
consistent with our expectations given the significant cultural and economic connections the
two countries share and the very short distance between each other.
{Insert Table 4 about here}
Table 5 summarizes the long-run and short-run price elasticity estimates. Again, in line with
demand theory, Australian tourists generally present higher degrees of responsiveness to
price changes in the long run than in the short run. For the very few exceptions the long-run
and short-run elasticity values are still very close. The diagonal entries are own-price
elasticities, and they are all negative and significant in both the long run and short run,
which are consistent with demand theory. For the majority of cases own-price elasticities
values are between 0 and -1, suggesting that the demand for tourism in these destinations
are price-inelastic, except for the US in the long run and New Zealand in both long run and
short run. In particular, the demand for Australian domestic tourism is the least price
sensitive in both the long run and the short run. This is in line with demand theory which
suggests that necessities tend to have more price-inelastic demand (Mankiw & Taylor, 2006).
This can also be explained by the relatively high portion of Australians’ trips for visiting
18
friends and relatives (VFR) purposes in domestic tourism compared to international tourism.
The demand for VFR tourism is generally less price sensitive than leisure tourism.
It is also noted that, in the short run the demand for tourism in Asia is less price elastic (-
0.418) than that for the US (-0.852) and the UK (-0.926). There are two possible reasons for
this. First, as Figure 1 shows, the price level in Asia is much lower than that in the other two
long-haul destinations; second, the destination of Asia is defined as a much broader region
than individual countries. According to demand theory, the wider the defined market, the
less price elastic the demand because it is harder to find close substitutes for broadly
defined products (Mankiw & Taylor, 2006). However, in the long run own-price elasticities
tend to converge to some extent and the differences are not significant any more. The
highest own-price elasticities (in absolute values, greater than 1 in both the long run and the
short run) are shown in the case of New Zealand. This suggests that Australian demand for
tourism in New Zealand is the most price elastic. This can be explained by the geographical
proximity of the two countries and the relatively low cost of changing travel plans. For
instance, a family that have planned a trip to the UK might not be willing to change their
travel plans due to short term exchange rate movements as the trip is likely to have been
planned thoroughly in advance. But the cost of changing travel plans to New Zealand, both
financially and in terms of effort, is a lot lower, hence the higher responsiveness to relative
price movements between the two countries.
{Insert Table 5 about here}
With regard to cross-price elasticities (the off-diagonal elements of Table 5), we find that the
long-run as well as short-run cross-price elasticities for Australia in the first column are all
positive and significant, indicating that once the tourism price decreases in the overseas
destinations, the demand for Australian domestic tourism are likely to be substituted by
outbound tourism to these destinations. The substitution effect is the strongest for Asia,
closely followed by New Zealand. This is not surprising, as both destinations are
geographically very close to Australia and provide many tourism products similar to those
provided in Australia. For instance the beaches of Bali or Thailand are considered by many
Australians as close substitutes for the beaches in the Gold Coast, and the ski resorts in the
south island of New Zealand as close substitutes for ski resorts in Australian alpine regions.
These results are also consistent with the patterns observed in Figures 4 and 5, where the
significant decrease in domestic tourism expenditure share is coincided most noticeably
with the significant increase of Asia’s tourism expenditure share.
In addition, negative own-price elasticities are observed between Asia and the UK in both
the long run and the short run. These results suggest that the two destinations are likely to
be complements for Australian tourists. Asia is the major connecting hub for Australians
travelling to the UK (and Europe in general). It is not uncommon for Australians to plan their
travel such that they spend time in both Asia (as the connecting stop-over) and the UK.
Therefore, once a trip to the UK via Asia is considered, price increases in one destination is
19
likely to lead to a reduction of spending in the other. Furthermore, relatively low degrees of
complementarity between Asia and the US and substitution between the US and the UK are
observed only in the short run. On the contrary, a low level of substitution between Asia and
New Zealand is detected only in the long run.
5. CONCLUSION
This study is the first attempt to apply a system-of equation demand model to explicitly
quantify the substitutability between domestic and outbound tourism. The empirical study
focuses on Australia as the country of origin for domestic and outbound tourism, given its
significantly widening tourism trade deficit in recent years. For the first time, the theoretical
sound AIDS model, in both its static and dynamic forms, is applied to the domestic-outbound
tourism substitution analysis. Since the traditional CPI-based relative tourism price variable
is not applicable for this analysis, a new innovative price variable based on the PPP index
published by the Penn World Table is developed. Short-run demand elasticities are
calculated based on the estimates of the theoretically restricted EC-AIDS, which assist a
scientific investigation of the substitution relationship between domestic and outbound
tourism.
The findings of this study provide confirmatory evidence on the substitutability between
Australia’s domestic tourism and outbound tourism in such key destinations as Asia, the UK
and the US. In addition, this study reveals that domestic tourism is regarded as a necessity
by Australians, and their demand for domestic tourism is less price elastic than that for
outbound tourism. These findings confirm the validity and necessity of Australia’s tourism
polies in promoting domestic tourism in recent years, such as the “See Australia” marketing
initiative along with the “Domestic Tourism Initiative” (through See Australia) launched since
1999, and the “No Leave No Life” campaign launched in 2009 (OECD, 2003, Commonwealth
of Australia, 2009).
Meanwhile, given the widening tourism trade deficit in recent years, this study calls for
further policy considerations to continue to promote domestic tourism and reduce tourism
trade deficit. Effective public-private partnership between Federal, State and Territory
governments and the tourism industry is crucial to drive a sustainable development of
domestic tourism. Industry stakeholders all need to play an active role in the development.
Greater collaboration between industry stakeholders throughout the tourism value chain
and knowledge transfer through best practice sharing will contribute to further
improvements of the industry’s overall performance. To maximise the benefits of the whole
tourism industry, greater attention should be paid to the seasonal and regional balance of
domestic tourism promotion. Discounting especially in low seasons is likely to be effective in
attracting the more price-sensitive, lower-spending domestic tourists. In the present global
economic recession, the downturn in inbound tourism led to excess capacities of tourism
20
service providers. In this case lowering prices domestically is likely to attract more domestic
demand to fill these excess capacities. In addition to pricing strategies, continuous product
innovations may reduce the substitutability of domestic tourism by outbound travel.
This study is subject to some limitations. Firstly, a number of key tourist destinations such as
other European and South American countries are omitted from this study. China, with
whom Australia is increasingly connected with both socially and economically, is also
excluded from this study due to lack of data. We hope that in the future sufficient amount of
data on these destinations will be made available by Tourism Research Australia. The second
limitation is related to the two-stage approach to cointegration analysis. Due to the small
sample constraint, the long-run and short-run models were estimated in two separate
stages. As a result, the estimated long-run elasticities may be subject to some degrees of
inaccuracy. In the future once the sample size allows, a one-stage cointegration method
should be employed. We also must acknowledge that consumer behaviour is ever changing
and these price elasticities are likely to shift over time. With greater data availability in the
future, we would like to extend our study to a time-varying parameter framework (see Li et
al., 2006), which will allow us to identify temporal patterns in the estimated price elasticities
for different destination regions.
References
Allen, D., Yap, G., &Shareef, R. (2009). Modelling interstate tourism demand in Australia: A cointegration analysis. Mathematics and Computers in Simulation, 79(9), 2733- 2740.
Anderson, G., & R. Blundell (1983).Testing restrictions in a flexible dynamic demand system: An application to consumers’ expenditure in Canada. Review of Economic Studies, 50, 397-410.
Ashworth, J., & Bergsma, J. R. (1987). New policies for tourism: Opportunities or problems? Tijdschriftvoor Economische en Sociale Geografie, 78(2), 151-152.
Ashworth, J., & Johnson, P. (1990). Holiday tourism expenditure: Some preliminary econometric results. Tourism Review, (3), 12-19.
Athanasopoulos, G., & Hyndman, R. J. (2008). Modelling and forecasting Australian domestic tourism. Tourism Management, 29, 19-31.
Australian Bureau of Statistics (2011). Tourism satellite account: Australian National Accounts,Catalogue No. 5249.0.
Baretje, R. (1982). Tourism's external account and the balance of payments. Annals of Tourism Research, 9(1), 57-67.
Buse, A., & Chan, W. H. (2000). Invariance, price indices and estimation in almost ideal demand systems.Empirical Economics,25(3), 519-39.
Chambers, M. J., & Nowman, K. B. (1997). Forecasting with the almost ideal demand system: Evidence from some alternative dynamic specifications. Applied Economics, 29, 935-43.
21
Commonwealth of Australia (2009). National Long-Term Tourism Strategy. Department of Resources, Energy and Tourism, Australian Government. Retrived August 14, 2012, from http://www.ret.gov.au/tourism/Documents/DRET%20Tourism%20Strategy.pdf.
Cortés-Jiménez, I., Durbarry, R., &Pulina, M. (2009). Estimation of out bound Italian tourism demand: A monthly dynamic EC-LAIDS model. Tourism Economics, 15, 547–565.
Crouch, G. I. (1996). Demand elasticities in international marketing: A meta-analysis application to tourism. Journal of Business Research, 36(2), 117-136.
Crouch, G. I., & Ritchie, J. R. B. (1999). Tourism, competitiveness, and societal prosperity. Journal of Business Research, 44, 137-152.
De Mello, M.M., Pack, A.,& Sinclair, M.T. (2002). A system of equations model of UK tourism demand in neighbouring countries. Applied Economics, 34, 509‐521.
Deaton, A., & Heston, A. (2010). Understanding PPPs and PPP-based national accounts. American Economic Journal: Macroeconomics, 2(4), 1-35.
Deaton, A.,& Muellbauer, J. (1980a). An almost ideal demand system. American Economic Review, 70(3), 312-326.
De Mello, M. M., & Fortuna, N. (2005). Testing alternative dynamic systems for modelling tourism demand. Tourism Economics, 11(4), 517–537.
Deng, M., & Athanasopoulos, G. (2011). Modelling Australian domestic and international inbound travel: a spatial–temporal approach.Tourism Management, 32(5), 1075-1084.
Divisekera, S. (2010). Economics of leisure and non-leisure tourist demand: A study of domestic demand for Australian tourism. Tourism Economics, 16(1), 117-136.
Duffy, M. (2003). Advertising and food, drink and tobacco consumption in the United Kingdom: A dynamic demand system. Agricultural Economics, 28(1), 21-70.
Durbarry, R., & Sinclair, M.T. (2003). Market shares analysis: The case of French tourism demand. Annals of Tourism Research, 30(4), 927-941.
Edgerton, D. L., Assarsson, B., Hummelmose, A., Laurila, I. P., Rickertsen, K., & Vale, P. H. (1996). The Econometrics of Demand Systems with Applications to Food Demand in the Nordic Countries. London: Kluwer Academic Publishers.
Eugenio-Martin, J. L. & Campos-Soria, J. A. (2011). Income and the substitution pattern between domestic and international tourism demand. Applied Economics, 43(20), 2519-2531.
Forsyth, P., & Dwyer, L. (2009). Tourism Price Competitiveness. The Travel & Tourism Competitiveness Report (pp. 77–90). World Economic Forum.
Fujii, E. T., Khaled, M., & Mak, J. (1985). An almost ideal demand system for visitor expenditures. Journal of Transport Economics and Policy, 19, 161-171.
Hamal, K. (1997). Substitutability between domestic and outbound travel in Australia. Pacific Tourism Review, 1(1), 23-33.
Han, Z., Durbarry, R., & Sinclair, M.T. (2006). Modelling US tourism demand for European destinations. Tourism Management, 27(1), 1-10.
Holmes, R. A., & Shamsuddin, A. F. M. (1997).Short- and long-term effects of world exposition 1986 on US demand for British Columbia tourism. Tourism Economics, 3, 137-60.
22
Huybers, T. (2003). Domestic tourism destination choices - A choice modelling analysis. International Journal of Tourism Research, 5, 445-459.
Kim, J. H., & Ngo, M. T. (2001).Modelling and forecasting monthly airline passenger flows among three major Australian cities. Tourism Economics, 7(4), 397-412.
Li, G., Song, H., & Witt, S. F. (2005). Recent development in econometric modeling and forecasting. Journal of Travel Research, 44(1), 82-99.
Li, G., Song, H., & Witt, S. F. (2004). Modeling tourism demand: A dynamic linear AIDS approach. Journal of Travel Research, 43(22), 141-150.
Li, G., Song, H., & Witt, S.F. (2006). Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting. International Journal of Forecasting, 22, 57-71.
Lyssiotou, P. (2000). Dynamic analysis of British demand for tourism abroad. Empirical Economics, 25(3), 421–436.
Mankiw, N. G., & Taylor, M. P. (2006). Economics. London: Thomson.
Organisation for Economic Co-operation and Development (OECD, 2003). National Tourism Policy Review of Australia. Paris: OECD.
Nicolau, J. L., & Más, J. M. (2005). Stochastic modeling: A three-stage tourist choice process. Annals of Tourism Research, 32(1), 49-69.
Papatheodorou, A. (1999). The demand for international tourism in the Mediterranean region. Applied Economics, 31(5), 619-630.
Pearce, D. G. (1987). Tourism Today: A Geographical Analysis. Harlow: Longman.
Pearce, D. G. (1989). International and domestic tourism: Interfaces and issues. GeoJournal, 19(3), 257-262.
Pollak, R. A., & Wales, T. J. (1992). Demand System Specification and Estimation. Oxford: Oxford University Press.
Pyo, S., Uysal, M., & McLellan, R. (1991). A linear expenditure model for tourism demand. Annals of Tourism Research, 18(3), 443–454.
Seckelmann, A. (2002). Domestic tourism—a chance for regional development in Turkey? Tourism Management, 23(1), 85–92.
Seetaram, N. (2012). Estimating demand elasticities for Australia’s international outbound tourism. Tourism Economics, 18(5), 999–1017.
Smeral, E., & Witt, S. F. (1996). Econometric forecasts of tourism demand to 2005. Annals of Tourism Research, 23(4), 891–907.
Song, H., & Li, G. (2008). Tourism demand modelling and forecasting: A review of recent research. Tourism Management, 29(2), 203–220.
Song, H., Li, G., Witt, S. F., & Fei, B. (2010). Tourism demand modelling and forecasting : how should demand be measured ? Tourism Economics, 16(1), 63–81.
Stabler, M. J., Papatheodorou, A., & Sinclair, M. T. (2010). The Economics of Tourism (2nd ed.). Routledge.
Sugiyarto, G., Blake, A., &Sinclair, M. T. (2003). Tourism and globalization: Economic impact in Indonesia. Annals of Tourism Research,30(3), 683–701.
Tornqvist, L. (1936). The bank of Finland’s consumption price index. Bank of Finland Monthly Bulletin, 10, 1–8.
23
Tourism Australia (2011). Exchange rates: challenges and opportunities for Australian tourism. Retrieved from www.tourism.australia.com/en-au/downloads/Exchange_Rates.pdf
Tourism Research Australia. (2010). Travel by Australians, June Quarter 2010. Tourism Australia, Canberra.
Tourism Research Australia (2011a). Factors affecting the inbound tourism sector: The impact and implications of the Australian dollar, Tourism Australia, Canberra.
Tourism Research Australia (2011b). What is driving Australians' travel choices? Tourism Australia, Canberra.
Tourism Research Australia (2011c). International Visitors in Australia: March 2011 Quarterly Results of the International Visitor Survey, Tourism Australia, Canberra.
Tribe, J. (2011). The economics of recreation, leisure & tourism, fourth edition. Oxford: Butterworth-Heinemann.
United Nations (1992). Handbook of the International Comparison Program, Series F No. 62, New York: United Nations.
van Soest, A., & Kooreman, P. (1987). A micro-econometric analysis of vacation behaviour. Journal of Applied Econometrics, 2(3), 215-226.
Witt, S. F., Newbould, G. D., & Watkins, A. J. (1992). Forecasting domestic tourism demand: application to Las Vegas arrivals data. Journal of Travel Research, 31(1), 36–41.
Wu, D. C., Li, G., & Song, H. (2011). Analyzing tourist consumption: A dynamic system-of-equations approach. Journal of Travel Research, 50(1), 46-56.
Yap, G., & Allen, D. (2011). Investigating other leading indicators influencing Australian domestic tourism demand. Mathematics and Computers in Simulation, 81(7), 1365-1374.
Zellner, A. (1962). An efficient method of estimating seemingly unrelated regressions and test for aggregation bias. Journal of the American Statistical Association, 57, 348-68.
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Table 1.Wald Test Statistics for Static and Dynamic AIDS Specifications
Null hypothesis STATIC-AIDS EC-AIDS
Homogeneity 62.096** 2.143
Symmetry conditional on Homogeneity 50.455** 15.410*
Both homogeneity and symmetry 128.083** 5.173
Note: * and** denote that the null hypothesis is rejected at the 5% and 1% significance levels, respectively.
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Table 2.Estimation Results of the STATIC-AIDS
AUS ASIA USA UK
𝑙𝑛 (𝑃(𝐴𝑈𝑆)̂̂𝑡|2005) − 𝑙𝑛 (𝑃(𝑁𝑍)̂̂
𝑡|2005) -0.187** (-8.351)
0.085** (6.944)
0.040** (3.426)
0.016* (2.177)
𝑙𝑛 (𝑃(𝐴𝑆𝐼𝐴)̂̂𝑡|2005) − 𝑙𝑛 (𝑃(𝑁𝑍)̂̂
𝑡|2005) 0.085** (6.944)
-0.003 (-0.1751)
-0.026* (-2.073)
-0.034** (-4.483)
𝑙𝑛 (𝑃(𝑈𝑆𝐴)̂̂𝑡|2005) − 𝑙𝑛 (𝑃(𝑁𝑍)̂̂
𝑡|2005) 0.040** (3.426)
-0.026* (-2.073)
-0.013 (-1.048)
0.009 (1.371)
𝑙𝑛 (𝑃(𝑈𝐾)̂̂𝑡|2005) − 𝑙𝑛 (𝑃(𝑁𝑍)̂̂
𝑡|2005) 0.016* (2.177)
-0.034** (-4.483)
0.009 (1.371)
0.005 (0.784)
𝑙𝑛 (𝑥𝑡
𝑝𝑡)
-0.138 (-1.553)
0.059 (1.539)
0.051 (1.293)
0.036 (1.371)
Note: * and** denote 5% and 1% significance levels, respectively. t-statistics are included in the parentheses.
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Table 3.Estimation Results of the EC-AIDS
AUS ASIA USA UK
∆ [𝑙𝑛 (�̂̂�𝐴𝑈𝑆,𝑡|2005) − 𝑙𝑛 (�̂̂�𝑁𝑍,𝑡|2005)] -0.052 (-1.139)
0.015 (0.722)
0.000 (0.019)
0.008 (0.422)
∆ [𝑙𝑛 (�̂̂�𝐴𝑆𝐼𝐴,𝑡|2005) − 𝑙𝑛 (�̂̂�𝑁𝑍,𝑡|2005)] 0.015 (0.722)
0.041** (2.854)
-0.021 (-1.908)
-0.035** (-3.365)
∆ [𝑙𝑛 (�̂̂�𝑈𝑆,𝑡|2005) − 𝑙𝑛 (�̂̂�𝑁𝑍,𝑡|2005)] 0.000 (0.019)
-0.021 (-1.908)
0.007 (0.473)
0.023* (2.138)
∆ [𝑙𝑛 (�̂̂�𝑈𝐾,𝑡|2005) − 𝑙𝑛 (�̂̂�𝑁𝑍,𝑡|2005)] 0.008 (0.422)
-0.035** (-3.365)
0.023* (2.138)
0.001 (0.067)
∆ [𝑙𝑛 (𝑥𝑡
𝑝𝑡)]
-0.052 (-0.908)
0.018 (0.669)
0.032 (1.098)
0.016 (0.770)
EC term (same for all equations) -0.621** (-9.153)
-0.621** (-9.153)
-0.621** (-9.153)
-0.621** (-9.153)
Note: * and** denote 5% and 1% significance levels, respectively. t-statistics are included in the parentheses.
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Table 4.Long-Run and Short-Run Expenditure Elasticity Estimates
Note: ** and * denote 1% and 5% significance levels, respectively.
AUS ASIA USA UK NZ
Long-run 0.822** 1.726** 1.914** 1.681** 0.803*
Short-run 0.933** 1.222** 1.570** 1.299** 0.630*
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Table 5.Long-Run and Short-Run Compensated Price Elasticity Estimates
AUS ASIA USA UK NZ
AUS LR -0.464** 1.832** 1.495** 1.081* 2.101**
SR -0.290** 0.960** 0.784* 0.935* 1.596** ASIA LR 0.191** -0.954** -0.575** -0.547*
SR 0.100** -0.418* -0.298 -0.583** USA LR 0.107** -1.180**
SR 0.056* -0.825** 0.502*
UK LR 0.073** -0.372** -0.848** SR 0.063* -0.377** 0.474* -0.926**
NZ LR 0.094** -0.236* -1.538**
SR 0.071** -1.570**
Note: * and** denote 5% and 1% significance levels, respectively. LR and SR indicate long-run and short-run elasticities, respectively. Statistically insignificant elasticities are omitted from the table.
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Figure 1. Domestic and Outbound Tourism Consumption of Australians as a Percentage of Household Final Consumption Expenditure
Sources: Australian Bureau of Statistics, Catalogue number 5249.0 - Australian National Accounts: Tourism Satellite Account, 2010-11 and Australian Bureau of Statistics, Catalogue number 5206.0 - Australian National Accounts: National Income, Expenditure and Product, Mar 2012
30
Figure 2. Quarterly Prices as constructed using Equation (4).
31
Figure 3. Yearly Modified Relative Price Levels of GDP for Period 2000-2009
32
Figure 4: Australian Domestic Tourism Budget Share over the Period 2000Q1-2010Q3
33
Figure5: Budget Shares of Overseas Destinations over the Period 2000Q1-2010Q3
34
Figure6. Australian Domestic Price Variables
Note: 𝑷𝒚𝒓are the observed annual values as specified in Equation (5); �̂�𝒒𝒓𝒕are the estimated
quarterly values as specified in Equation (7); �̂̂�𝒒𝒓𝒕are the modified estimated quarterly values as specified by Equation (9).
35
Figure 7. Quarterly Modified Relative Price Levels of GDP